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Computational Inference for Directions in Canonical Correlation Analysis
Daniel Kessler, Elizaveta Levina
Canonical Correlation Analysis (CCA) is a method for analyzing pairs of random vectors; it learns a sequence of paired linear transformations such that the resultant canonical vari…
Fair Information Spread on Social Networks with Community Structure
Octavio Mesner, Elizaveta Levina, Ji Zhu
Information spread through social networks is ubiquitous. Influence maximiza- tion (IM) algorithms aim to identify individuals who will generate the greatest spread through the soc…
A pseudo-likelihood approach to community detection in weighted networks
Andressa Cerqueira, Elizaveta Levina
Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns. While many community detection methods have been develope…
Conformal Prediction for Network-Assisted Regression
Robert Lunde, Elizaveta Levina, Ji Zhu
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventiona…